The present invention relates to determination of the quality of sleep of a subject under test and, more particularly, determination of that quality from various physiological data obtained from the subject during such sleep.
Substantial research has been undertaken directed toward understanding the nature of sleep and sleep disorders. This research has yielded considerable information concerning human patterns of sleeping and not sleeping, and of physiological activities occurring in humans during sleep. In addition, substantial information has been obtained concerning various sleep disorders.
In assessing the physiological activity occurring during sleep, various kinds of signal data from sensors on the subject are typically obtained, recorded and analyzed. Primary kinds of data obtained for determining sleep disorders are electrophysiological signals such as electroencephalographic signals, and transducer signals resulting from the detection of other kinds of physiological parameters such as signals characterizing respiratory performance. Other commonly measured electrophysiological signals are electrocardiogram signals and electromyographic signals. Other kinds of physiological parameter signals typically obtained by sensing transducers are blood oxygen saturation signals, limb movement or activity signals, and the acoustic signals arising from snoring. Such signals are typically recorded over a substantial duration of the subject's sleep and so provide rather voluminous records.
As a result, computer based storage of such records is attractive, as are computer based analyses of such signal records to determine the occurrence in each of clinically significant events. These significant events in the signal records in such analyses are to be searched for by the computer, and are defined for each particular signal based on criteria specified by the analyst that describe the waveform portion structures of clinical interest in that signal. The computer reviews the physiological parameter signal records that were recorded over the sleeping time of the subject under test to determine which portions thereof meet the specified criteria to thereby determine the occurrence of significant events in that signal. Each such waveform portion thus found in each signal as a significant event therefor is then marked and counted.
Unfortunately, there are no presently agreed upon criteria by sleep analysis professionals, or polysomnographic professionals and technologists, which can be relied upon for each signal record to select all the significant events from each of the signals measured and recorded over the subject's time of sleeping. In fact, there is not yet a general consensus as to what constitutes clinically relevant structure portions in the signals. Furthermore, sleep analysts often are interested in confirming the presence of a significant event in a parameter signal waveform by the closeness of its association with other significant events in other parameter signal waveforms rather than just being interested in the magnitudes of events in the initial parameter waveform. Such an association between significant events in parameter signal waveforms can confirm the occurrence of an event in one of those waveforms even though it may be relatively mild in severity, that is, in magnitude and duration. As an example, respiratory events which appear less severe in the signal waveform obtained for that parameter may still have significant implications if they are associated with electroencephalographic signal arousal events since the resulting sleep fragmentation can cause daytime sleepiness in the subject which can lead to poorer activity performances and to various kinds of accidents.
Such less severe, but clinically relevant, events are recognized by sleep analysts as possibly going undetected if the only detection method therefor is the setting of various thresholds to thereby independently define significant events in each of the parameter signals without regard to events in the other signals. Thus, such analysts often feel forced to review the entire set of polysomnographic signals over time to be certain that no clinically relevant events are lost if such thresholds are set to be quite stringent, that is, to give a relatively high probability of capturing just those signal portion structures which are quite certain to be of clinical interest. Of course, less stringent thresholds could be set to sort significant events from the remainder of the time signals for the parameters being measured, but the result may well be that too many artifacts in the signals are found as events leading to finding too many false positives as significant events. Thus, the analysts again would often feel compelled to review all of the parameter signals over time to eliminate falsely reported significant events in the parameter signals. Hence, there is a desire for a system which can avoid any need to review of all polysomnographic signals over time while providing the user with a cumulation of clinically relevant events in the parameter signals substantially separated from other events occurring in those signals.